A Novel Conditional Diffusion-Based Framework for Advanced Reconstruction of Cloud Vertical Structure

This study presents a framework based on conditional diffusion probabilistic models for reconstructing vertical cloud structures from passive satellite remote sensing observations. The retrieval is formulated as a conditional denoising diffusion process, in which randomly initialized latent fields are progressively refined through iterative sampling guided by moderate resolution imaging spectroradiometer (MODIS) measurements. Compared with generative adversarial network (GAN)-based approaches, the proposed model more effectively captures the intrinsic variability, multiscale organization, and stochastic nature of atmospheric cloud fields. It exhibits superior performance in reconstructing complex multilayer systems, intense convective structures, and mesoscale cloud features. Quantitative evaluation against CloudSat radar reflectivity data demonstrates that the method consistently attains high structural similarity and accurately reproduces the vertical distribution of cloud reflectivity. These findings indicate that conditional diffusion probabilistic models provide a novel generative modeling paradigm for atmospheric remote sensing, providing physically consistent reconstructions, quantitative uncertainty characterization, and robust generalization to diverse atmospheric regimes. Furthermore, the framework can be extended to the 3-D reconstruction of other meteorological variables, supporting broader application of generative AI in satellite-based atmospheric analyses.